{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_path = './0.jpg'\n",
    "pre_path = './0_predict.png'\n",
    "img = cv2.imread(image_path)\n",
    "pre = cv2.imread(pre_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x214b40340b8>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1,2,1)\n",
    "plt.imshow(img)\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(pre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x214bef0db00>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x214c26ff438>"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#腐蚀操作\n",
    "kernel = np.array([[0,1,0],[1,1,1],[0,1,0]],np.uint8)\n",
    "erosion = cv2.erode(pre,kernel,iterations = 1)\n",
    "# plt.imshow(erosion)\n",
    "#转换后发现边缘\n",
    "imgray = cv2.cvtColor(erosion,cv2.COLOR_BGR2GRAY)\n",
    "ret,thresh = cv2.threshold(imgray,127,255,0)\n",
    "im2, contours = cv2.findContours(thresh,1,2)\n",
    "\n",
    "#分割后逐个膨胀操作\n",
    "dc = cv2.drawContours(np.zeros(imgray.shape), im2, 14, (1,1,1), -1)\n",
    "dilation = cv2.dilate(dc,kernel,iterations = 6)\n",
    "dilation = np.stack([dilation,dilation,dilation])\n",
    "\n",
    "#计算相应的掩膜图像\n",
    "result = img*dilation.transpose([1,2,0])\n",
    "plt.imshow(result/255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        ...,\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0]],\n",
       "\n",
       "       [[  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        ...,\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0]],\n",
       "\n",
       "       [[  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        ...,\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        ...,\n",
       "        [255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        [255, 255, 255]],\n",
       "\n",
       "       [[  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        ...,\n",
       "        [255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        [255, 255, 255]],\n",
       "\n",
       "       [[  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        [  0,   0,   0],\n",
       "        ...,\n",
       "        [255, 255, 255],\n",
       "        [255, 255, 255],\n",
       "        [255, 255, 255]]], dtype=uint8)"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(im2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parseMask(img,mask):\n",
    "    '''\n",
    "    :param img: W*H*channle,0-255\n",
    "    :param mask: W*H,0-255\n",
    "    :return:yield every mask img\n",
    "    '''\n",
    "    # 腐蚀操作\n",
    "    kernel = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]], np.uint8)\n",
    "    erosion = cv2.erode(pre, kernel, iterations=1)\n",
    "    # plt.imshow(erosion)\n",
    "    # 转换后发现边缘\n",
    "    imgray = cv2.cvtColor(erosion, cv2.COLOR_BGR2GRAY)\n",
    "    ret, thresh = cv2.threshold(imgray, 127, 255, 0)\n",
    "    contours, hie = cv2.findContours(thresh, 1, 2)\n",
    "\n",
    "    for i,cnt in enumerate(contours):\n",
    "        # 分割后逐个膨胀操作\n",
    "        dc = cv2.drawContours(np.zeros(imgray.shape), np.array([cnt]), 0 , (1, 1, 1), -1)\n",
    "        dilation = cv2.dilate(dc, kernel, iterations=5)\n",
    "        if img.ndim==3:\n",
    "            dilation = np.stack([dilation]*img.shape[-1])\n",
    "\n",
    "        # 计算相应的掩膜图像\n",
    "        result = img * dilation.transpose([1, 2, 0])\n",
    "        plt.imshow(result / 255)\n",
    "\n",
    "        x, y, w, h = cv2.boundingRect(cnt)\n",
    "        cut = result[y - 5:y + h + 5, x - 5:x + w + 5, :]\n",
    "        \n",
    "        yield cut.astype(np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "cut = parseMask(img,pre)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x214ca64b0f0>"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(next(cut))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
